Downstream NLP tasks on ASR transcripts need to be robust to noise due to the commonality of transcription errors. Robustness to noise is often evaluated by constructing a baseline result with clean text and then injecting varying degrees of noise to the text and examining how the result changes  [1]}, [2]}. To investigate the effects of noise, it is necessary to select different ways of injecting plausible ASR noise into transcripts. A recent study explored the feasibility of improving the robustness of speech-enabled systems with three methods of noise [3]}; rule-based substitution which randomly substitutes a candidate word with a phonetically similar one, statistic-based confusion substitution which samples replacement words from a pre-constructed ASR confusion matrix and finally, model-based substitution utilizing a generative GPT model to directly produce ASR-like text. Another study investigated the stability of topics over noisy sources, by testing for topic model agreements [4]}, after subjecting the training data to insertion of frequent words, deletion and rule-based phonetic substitution errors [1]}.
